Models based on partial differential equations containing time–space fractional derivatives have attracted considerable interest in the past decade because of their ability to model anomalous ...transport phenomena. These phenomena are strongly connected to the interactions within complex and non-homogeneous media exhibiting spatial heterogeneity. The class of equations with multi-term time–space derivatives of fractional orders has been found to be very useful in the description of such interactions. This motivates the extension of the classical Bloch–Torrey equation through the application of the operators of fractional calculus to new multi-term time–space fractional Bloch–Torrey equations with Riesz fractional operators.
In this paper, we firstly propose an unstructured-mesh Galerkin finite element method for the two-dimensional multi-term time–space fractional diffusion equation with Riesz fractional operators on irregular convex domains. Secondly, we rigorously establish the stability and convergence of the numerical scheme. Thirdly, we extend the computational model to solve a system of coupled two-dimensional multi-term time–space fractional Bloch–Torrey equations. Finally, some numerical results are given to demonstrate the versatility and application of the models.
This paper introduces a novel, region-growing algorithm for the fast surface patch segmentation of three-dimensional point clouds of urban environments. The proposed algorithm is composed of two ...stages based on a coarse-to-fine concept. First, a region-growing step is performed on an octree-based voxelized representation of the input point cloud to extract major (coarse) segments. The output is then passed through a refinement process. As part of this, there are two competing factors related to voxel size selection. To balance the constraints, an adaptive octree is created in two stages. Empirical studies on real terrestrial and airborne laser scanning data for complex buildings and an urban setting show the proposed approach to be at least an order of magnitude faster when compared to a conventional region growing method and able to incorporate semantic-based feature criteria, while achieving precision, recall, and fitness scores of at least 75% and as much as 95%.
Nowadays, society is growing and crowded, the construction of automatic smart waste sorter machine utilizing the intelligent sensors is important and necessary. To build this system, trash ...classification from trash images is an important issue in computer vision to be addressed for integrating into sensors. Therefore, this study proposes a robust model using deep neural networks to classify trash automatically which can be applied in smart waste sorter machines. Firstly, we collect the VN-trash dataset that consists of 5904 images belonging to three different classes including Organic, Inorganic and Medical wastes from Vietnam. Next, this study develops a deep neural network model for trash classification named DNN-TC which is an improvement of ResNext model to improve the predictive performance. Finally, the experiments are conducted to compare the performances of DNN-TC and the state-of-the-art methods for trash classification on VN-trash dataset as well as Trashnet dataset to show the effectiveness of the proposed model. The experimental results indicate that DNN-TC yields 94% and 98% in terms of accuracy for Trashnet and VN-trash datasets respectively and thus it outperforms the state-of-the-art methods for trash classification on both experimental datasets.
Severinia buxifolia (Rutaceae) is a promising source of bioactive compounds since it has been traditionally used for the treatment of various diseases. The present study aimed at evaluating the ...impact of different solvents on extraction yields, phytochemical constituents and antioxidants, and in vitro anti-inflammatory activities of S. buxifolia. The results showed that the used solvents took an important role in the yield of extraction, the content of chemical components, and the tested biological activities. Methanol was identified as the most effective solvent for the extraction, resulting in the highest extraction yield (33.2%) as well as the highest content of phenolic (13.36 mg GAE/g DW), flavonoid (1.92 mg QE/g DW), alkaloid (1.40 mg AE/g DW), and terpenoids (1.25%, w/w). The extract obtained from methanol exhibited high capacity of antioxidant (IC50 value of 16.99 μg/mL) and in vitro anti-inflammatory activity (i.e., albumin denaturation: IC50 = 28.86 μg/mL; antiproteinase activity: IC50 = 414.29 μg/mL; and membrane stabilization: IC50 = 319 μg/mL). The antioxidant activity of the S. buxifolia extract was found to be 3-fold higher than ascorbic acid, and the anti-inflammatory activity of S. buxifolia extract was comparable to aspirin. Therefore, methanol is recommended as the optimal solvent to obtain high content of phytochemical constituents as well as high antioxidants and in vitro anti-inflammatory constituents from the branches of S. buxifolia for utilization in pharmacognosy.
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•Gastroretentive floating tablets were developed using HME coupled FDM 3D printing.•HPC and PVP VA 64 in combination produced 3D printable filaments.•Extruded filaments possessed good ...mechanical strength suitable for 3D printing.•3D design of tablets has significant impact on drug release characteristics.•Buoyancy kinetics of 3D printed floating tablets.
Three-dimensional printing could serve as a platform to fabricate individualized medicines and complex-structured solid dosage forms. Herein, hot melt extrusion was coupled with 3D printing to develop a unique gastro retentive dosage form to personalize treatment of cinnarizine or other narrow absorption window drugs. The mechanical strength of the extruded strands was optimized for printing by combining two polymers, hydroxypropyl cellulose and vinylpyrrolidone vinyl acetate copolymer. The unit dose, floating force, and release profile were controlled by the printing parameters and object design. The tablets floated immediately within the FaSSGF, and floating force was relatively constant up to 12 h. Drug release followed zero-order kinetics and could be controlled from 6 h to ≥ 12 h. Input variables had a good correlation (R > 0.95) with unit dose, floating force, and dissolution profile (p < 0.05). Authors successfully proposed and tested a new paradigm of individualized medicine fabrication to meet individual patient needs.
Sequential model‐based design of experiments (MDBOE) accounts for information from previous experiments when selecting conditions for new experiments. In the current study, sequential MBDOE is used ...to select operating conditions for experiments in a batch‐reactor that produces bio‐based polytrimethylene ether glycol (PO3G). These Bayesian A‐optimal experiments are designed to obtain improved estimates of 70 fundamental‐model parameters, while accounting for industrial data from eight previous runs. Settings are selected for three decision variables: reactor temperature, initial catalyst level, and initial water concentration. If only one new experiment is conducted, it should be run at high temperature, with relatively high concentrations of catalyst and initial water. When two new runs are conducted, one should use an intermediate catalyst concentration. The effectiveness of the proposed MBDOE approach is tested using Monte‐Carlo simulations, revealing that the selected experiments are superior compared to experiments selected randomly from corners of the permissible design space.
Field experiments of solute transport through heterogeneous porous and fractured media show that the growth of contaminant plumes may convert between diffusive states. In this paper, we propose a ...multi-term time–space variable-order fractional advection–diffusion model (MTT-SVO-FADM) to describe the underlying transport dynamics. We consider a numerical approach based on the implicit numerical method for numerical solution of this model. A fully-discrete numerical scheme is developed by using the classical finite difference method. The unconditional stability and convergence of the scheme are discussed and theoretically proved. We use a modified grid approximation method (MGAM) to estimate the model’s parameters. The MTT-SVO-FADM is then applied to describe transient dispersion observed at a field tracer test and four numerical experiments. The results show that this model can simulate the experimental data more accurately and can efficiently quantify these transitions.
•A multi-term time–space variable-order fractional advection–diffusion model is proposed.•A fully-discrete numerical scheme is developed.•The unconditional stability and convergence of the scheme are discussed and proved.•A modified grid approximation method is used to estimate the model’s parameters.•This model is applied to describe transient dispersion observed at a field tracer test and four numerical experiments.
•Cu(BDC) was used as catalyst for the modified Friedländer.•High conversions were achieved using catalytic amounts of the Cu(BDC).•The catalyst could be recovered and reused.
A crystalline porous ...metal–organic framework Cu(BDC) was synthesized, and characterized by several techniques, including X-ray powder diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), thermogravimetric analysis (TGA), Fourier transform infrared (FT-IR), atomic absorption spectrophotometry (AAS), hydrogen temperature-programmed reduction (H2-TPR), and nitrogen physisorption measurements. The Cu(BDC) exhibited high catalytic activity for the modified Friedländer transformation using 2-aminobenzyl alcohol as the starting material, thus offering advantages over the conventional Friedländer reaction in terms of avoiding the problems associated with the storage of the highly unstable 2-aminobenzaldehyde. Moreover, the Cu(BDC) could offer significantly higher catalytic activity than that of other Cu-MOFs such as Cu3(BTC)2, Cu(BPDC), and Cu2(BDC)2(DABCO). The catalyst could be recovered and reused several times without a significant degradation in catalytic activity. The modified Friedländer reaction could only occur in the presence of the solid Cu(BDC) with no contribution from leached active species.
Hydrogen sulfide (H2S) is a noxious, potentially poisonous, but necessary gas produced from sulfur metabolism in humans. In Down Syndrome (DS), the production of H2S is elevated and associated with ...degraded mitochondrial function. Therefore, removing H2S from the body as a stable oxide could be an approach to reducing the deleterious effects of H2S in DS. In this report we describe the catalytic oxidation of hydrogen sulfide (H2S) to polysulfides (HS2+n−) and thiosulfate (S2O32−) by poly(ethylene glycol) hydrophilic carbon clusters (PEG‐HCCs) and poly(ethylene glycol) oxidized activated charcoal (PEG‐OACs), examples of oxidized carbon nanozymes (OCNs). We show that OCNs oxidize H2S to polysulfides and S2O32− in a dose‐dependent manner. The reaction is dependent on O2 and the presence of quinone groups on the OCNs. In DS donor lymphocytes we found that OCNs increased polysulfide production, proliferation, and afforded protection against additional toxic levels of H2S compared to untreated DS lymphocytes. Finally, in Dp16 and Ts65DN murine models of DS, we found that OCNs restored osteoclast differentiation. This new action suggests potential facile translation into the clinic for conditions involving excess H2S exemplified by DS.
Oxidized carbon nanozymes catalytically oxidize hydrogen sulfide to polysulfides and thiosulfate in an oxygen‐dependent manner. In this report the reaction kinetics of polysulfide and thiosulfate formation both in a cell‐free setting and intracellularly and present the possible role of these nanozymes as a supportive therapy in Down Syndrome, a condition where hydrogen sulfide production is detrimentally elevated.
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